Recent top-performing object detectors usually depend on a two-stage approach, which benefits from its region proposal and refining practice but suffers low detection speed. By contrast, one-stage approaches have the advantage of high efficiency while sacrifice their accuracies to some extent. In this paper, we propose a novel single-shot object detection network which inherits the merits of both. Motivated by the idea of semantic enrichment to the convolutional features within a typical deep detector, we propose two novel modules: 1) by modeling the semantic interactions between channels and the long-range dependencies between spatial positions, the self-attending module generates both channel and position attention, and enhance the original convolutional features in a self-guided manner; 2) leveraging the class-discriminative localization ability of classification-trained CNN, the semantic activating module learns a semantic meaningful convolutional response which augments low-level convolutional features with strong class-specific semantic information. The so called self-attending and semantic activating network (ASAN) achieves better accuracy than two-stage methods and is able to fulfil real-time processing. Comprehensive experiments on PASCAL VOC indicates that ASAN achieves state-of-the-art detection performance with high efficiency.

Selection relaying is a promising technique for practical implementation of cooperative systems with multiple relay nodes. However, to select the best relay, global channel knowledge is required at the selecting entity, which may result in considerable signaling overhead. In this paper, we consider the relay selection problem in dual-hop amplify-and-forward (AF) communication systems with partial channel state information (CSI). Relay selection strategies aiming at minimizing either the outage probability or the bit error rate (BER) with quantized CSI available are presented. We also propose a target rate based quantizer to efficiently partition the SNR range for outage minimized relay selection, and a target BER based quantizer for BER minimized relay selection. Simulation results show that near optimal performance is achievable with a few bits feedback to the selecting entity.

Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.

In this letter, we consider robust beamforming optimization for a multiuser multiple-input single-output system with simultaneous wireless information and power transmission (SWIPT) for the case of imperfect channel state information. Adopting the ellipsoidal uncertainty on channel vector, the robust beamforming design are reformulated as convex semi-definite programming (SDP) by rank-one relaxation. To reduce the complexity, an ellipsoidal uncertainty on channel covariance is studied to derive the equivalent form of original problem. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in two-way relay channels where two users exchange information with each other via a multi-antenna relay node. The signals forwarded by the relay node are also used to supply the power to two users. We formulate a max-min optimization problem aiming to maximize the minimum harvested energy between two users to achieve fairness. We jointly optimize the relay beamforming matrix and allocating powers at the two users subject to the quality of service (QoS) constraints. To be specific, we consider the amplify-and-forward (AF) relay strategy and the time splitting SWIPT strategy. To this end, we propose two different time splitting protocols to enable relay to supply power to two users. To solve the non-convex joint optimization problem, we propose to split the original optimization problem into two subproblems and solving them iteratively to obtain the final solution. It is shown that the first subproblem dealing with the beamforming matrix can be optimally solved by using the technique of relaxed semidefinite programming (SDR), and the second subproblem, which deals with the power allocation, can be solved via linear programming. The performance comparison of two schemes as well as the one-way relaying scheme are provided and the effectiveness of the proposed schemes is verified.

This paper presents a synchronization mechanism to effectively implement the lock and barrier protocols in a decentralized manner through explicit message passing. In the proposed solution, a simple and efficient synchronization control mechanism is proposed to support queued synchronization without contention. By using state-of-the-art Application-Specific Instruction-set Processor (ASIP) technology, we embed the synchronization functionality into a baseline processor, making the proposed mechanism feature ultra-low overhead. Experimental results show the proposed synchronization achieves ultra-low latency and almost ideal scalability when the number of processors increases.